tetano
Editor, Senior Moderator
Sci Rep
. 2024 Jan 4;14(1):534.
doi: 10.1038/s41598-023-47038-3. Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs
Ahmad MohdAziz Hussein[SUP] 1 [/SUP], Abdulrauf Garba Sharifai[SUP] 2 [/SUP], Osama Moh'd Alia[SUP] 3 [/SUP], Laith Abualigah[SUP] 4 5 6 7 8 9 [/SUP], Khaled H Almotairi[SUP] 10 [/SUP], Sohaib K M Abujayyab[SUP] 11 [/SUP], Amir H Gandomi[SUP] 12 13 [/SUP]
Affiliations
The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.
. 2024 Jan 4;14(1):534.
doi: 10.1038/s41598-023-47038-3. Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs
Ahmad MohdAziz Hussein[SUP] 1 [/SUP], Abdulrauf Garba Sharifai[SUP] 2 [/SUP], Osama Moh'd Alia[SUP] 3 [/SUP], Laith Abualigah[SUP] 4 5 6 7 8 9 [/SUP], Khaled H Almotairi[SUP] 10 [/SUP], Sohaib K M Abujayyab[SUP] 11 [/SUP], Amir H Gandomi[SUP] 12 13 [/SUP]
Affiliations
- PMID: 38177156
- DOI: 10.1038/s41598-023-47038-3
The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.